{"id":"W4240676231","doi":"10.9707/1944-5660.1431","title":"Front Matter","year":2018,"lang":"en","type":"paratext","venue":"The Foundation Review","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Impact","funders":"","keywords":"Front (military); Political science; Geography; Meteorology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008396583,0.001064209,0.0006780949,0.001904723,0.0009397544,0.004544215,0.0009677344,0.002876002,0.7864345],"category_scores_gemma":[0.00528583,0.0003473125,0.0004527606,0.001497465,0.0009010358,0.002218062,0.00222216,0.001985348,0.7070553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001363375,"about_ca_system_score_gemma":0.003305316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003032664,"about_ca_topic_score_gemma":0.005025893,"domain_scores_codex":[0.9995568,0.0000607928,0.00002561366,0.00007775889,0.0001804675,0.00009841556],"domain_scores_gemma":[0.998383,0.0002838798,0.0001346524,0.0001289983,0.0005671683,0.0005024148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002237231,0.000009066825,0.0000597791,0.0001973208,0.000003074593,0.00002232674,0.00001118424,0.00001716772,0.0000669099,0.002155667,0.8947417,0.1026935],"study_design_scores_gemma":[0.000007630874,0.000006520339,0.0001162469,0.0002376401,0.000002284556,0.00003598393,0.00001702246,0.000009691383,0.00002283629,0.0005116245,0.9990306,0.000001907733],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000491887,0.01916099,0.0006847716,0.02204244,0.02167769,0.0001002208,0.004528475,0.001030232,0.9302834],"genre_scores_gemma":[0.001964495,0.008013983,0.0003313579,0.004515598,0.002629603,0.00005257594,0.001170421,0.0002134805,0.9811086],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2135655,"threshold_uncertainty_score":0.3046253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04459250040447666,"score_gpt":0.3489237234350734,"score_spread":0.3043312230305967,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}